One of the biggest challenges facing the world today is the availability of clean and safe water. Growing population, industrialization and climate change have rapidly increased the demand for water. Desalination plants are used to make seawater drinkable. Reverse Osmosis (RO) membranes are used in these plants to purify water.
But it is very difficult to estimate the correct time of cleaning and replacement of these membranes. Cleaning or replacement at the wrong time can increase costs, affect water quality and disrupt plant operations.
To solve this challenge, Veolia Water Technologies, in collaboration with Amazon Web Services (AWS), developed a smart solution based on machine learning.
Why is machine learning important?
Traditional methods of membrane testing and maintenance are often guesswork. For example:
- When should cleaning be done,
- When should the membrane be replaced,
- or which part might wear out sooner.
These questions were answered not based on data, but on experience and guesswork. As a result – sometimes cleaning is done early, leading to unnecessary costs, and sometimes it is done late, leading to damage to the membrane.
Machine learning solves these problems as it analyzes historical data to learn patterns and predict future changes.
How does this system work?
Veolia worked with AWS to create a system that can predict the condition of the membrane using data collected over time.
Data collection
Time-series data from the last three years was collected. This included information on water pressure, conductivity, temperature, and other indicators.
Data Cleaning
Raw data often contains noise or incorrect values. These were removed to create a clean data set.
Data Normalization
Data was normalized to reduce the impact of external factors such as temperature or changes in water quality.
Machine Learning Model
Machine learning models were trained using AWS SageMaker and DeepAR algorithms.
Future Prediction
The model predicts by looking at past patterns when fouling will increase on the membrane, when cleaning will be needed and when it will have to be replaced.
Oman’s Sur Desalination Plant – A Successful Example
This system was first implemented at the Sur Desalination Plant in Oman.
- This plant cleans 1,32,000 cubic meters of sea water every day and provides water to more than 6 lakh people.
- Veolia Bahwan operates this plant.
Before the implementation of the machine learning model, the plant team had to manually check the data, which was a 12-hour job. Now it is done in just two clicks.
Benefits of the system
Predictive Maintenance
With the help of machine learning, it can be predicted when the membrane needs to be cleaned. This reduces the chances of sudden failure or emergency shutdown.
Saving time and cost
Unnecessary cleaning and use of additional chemicals can be prevented.
Better decision making
Operators get data in real time, which helps them take the right decision.
Improvement in water quality
Timely cleaning and maintenance increases the purity of water.
Why is machine learning a game-changer?
Machine learning is not limited to just reading data. It has the ability to predict the future, understand patterns and optimize processes.
For example –
If there is a slight increase in pressure on a particular day, a normal operator may ignore it.
But a machine learning model can recognize that pattern and tell that the problem of fouling may increase in the coming weeks.
Future prospects
Veolia has transferred the data to a cloud-based data lake and automated the entire process. In the future –
- Artificial Intelligence (AI)
- Internet of Things (IoT)
- and real-time monitoring
will be combined to make the system smarter.
This technology will not be limited to water plants only, but it can also be used in –
- Wastewater Management
- Industrial plants
- and agricultural irrigation systems.
Conclusion
The machine learning-based solution developed by Veolia and AWS has proved that future water management will be based on data and smart technology.
This not only makes operations more efficient but also saves both energy and cost. The Sur plant in Oman is a perfect example, where technology alone has improved water quality, stability and continuity of supply.
